superset world_bank 源码
superset world_bank 代码
文件路径：/superset/examples/world_bank.py
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""Loads datasets, dashboards and slices in a new superset instance"""
import json
import os
from typing import List
import pandas as pd
from sqlalchemy import DateTime, inspect, String
from sqlalchemy.sql import column
import superset.utils.database
from superset import app, db
from superset.connectors.sqla.models import SqlMetric
from superset.models.dashboard import Dashboard
from superset.models.slice import Slice
from superset.utils import core as utils
from superset.utils.core import DatasourceType
from ..connectors.base.models import BaseDatasource
from .helpers import (
get_example_url,
get_examples_folder,
get_slice_json,
get_table_connector_registry,
merge_slice,
misc_dash_slices,
update_slice_ids,
)
def load_world_bank_health_n_pop( # pylint: disable=too-many-locals, too-many-statements
only_metadata: bool = False,
force: bool = False,
sample: bool = False,
) -> None:
"""Loads the world bank health dataset, slices and a dashboard"""
tbl_name = "wb_health_population"
database = superset.utils.database.get_example_database()
engine = database.get_sqla_engine()
schema = inspect(engine).default_schema_name
table_exists = database.has_table_by_name(tbl_name)
if not only_metadata and (not table_exists or force):
url = get_example_url("countries.json.gz")
pdf = pd.read_json(url, compression="gzip")
pdf.columns = [col.replace(".", "_") for col in pdf.columns]
if database.backend == "presto":
pdf.year = pd.to_datetime(pdf.year)
pdf.year = pdf.year.dt.strftime("%Y-%m-%d %H:%M%:%S")
else:
pdf.year = pd.to_datetime(pdf.year)
pdf = pdf.head(100) if sample else pdf
pdf.to_sql(
tbl_name,
engine,
schema=schema,
if_exists="replace",
chunksize=50,
dtype={
# TODO(bkyryliuk): use TIMESTAMP type for presto
"year": DateTime if database.backend != "presto" else String(255),
"country_code": String(3),
"country_name": String(255),
"region": String(255),
},
method="multi",
index=False,
)
print("Creating table [wb_health_population] reference")
table = get_table_connector_registry()
tbl = db.session.query(table).filter_by(table_name=tbl_name).first()
if not tbl:
tbl = table(table_name=tbl_name, schema=schema)
tbl.description = utils.readfile(
os.path.join(get_examples_folder(), "countries.md")
)
tbl.main_dttm_col = "year"
tbl.database = database
tbl.filter_select_enabled = True
metrics = [
"sum__SP_POP_TOTL",
"sum__SH_DYN_AIDS",
"sum__SH_DYN_AIDS",
"sum__SP_RUR_TOTL_ZS",
"sum__SP_DYN_LE00_IN",
"sum__SP_RUR_TOTL",
]
for metric in metrics:
if not any(col.metric_name == metric for col in tbl.metrics):
aggr_func = metric[:3]
col = str(column(metric[5:]).compile(db.engine))
tbl.metrics.append(
SqlMetric(metric_name=metric, expression=f"{aggr_func}({col})")
)
db.session.merge(tbl)
db.session.commit()
tbl.fetch_metadata()
slices = create_slices(tbl)
misc_dash_slices.add(slices[-1].slice_name)
for slc in slices:
merge_slice(slc)
print("Creating a World's Health Bank dashboard")
dash_name = "World Bank's Data"
slug = "world_health"
dash = db.session.query(Dashboard).filter_by(slug=slug).first()
if not dash:
dash = Dashboard()
dash.published = True
pos = dashboard_positions
slices = update_slice_ids(pos)
dash.dashboard_title = dash_name
dash.position_json = json.dumps(pos, indent=4)
dash.slug = slug
dash.slices = slices
db.session.merge(dash)
db.session.commit()
def create_slices(tbl: BaseDatasource) -> List[Slice]:
metric = "sum__SP_POP_TOTL"
metrics = ["sum__SP_POP_TOTL"]
secondary_metric = {
"aggregate": "SUM",
"column": {
"column_name": "SP_RUR_TOTL",
"optionName": "_col_SP_RUR_TOTL",
"type": "DOUBLE",
},
"expressionType": "SIMPLE",
"hasCustomLabel": True,
"label": "Rural Population",
}
defaults = {
"compare_lag": "10",
"compare_suffix": "o10Y",
"limit": "25",
"granularity_sqla": "year",
"groupby": [],
"row_limit": app.config["ROW_LIMIT"],
"since": "2014-01-01",
"until": "2014-01-02",
"time_range": "2014-01-01 : 2014-01-02",
"markup_type": "markdown",
"country_fieldtype": "cca3",
"entity": "country_code",
"show_bubbles": True,
}
return [
Slice(
slice_name="Region Filter",
viz_type="filter_box",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="filter_box",
date_filter=False,
filter_configs=[
{
"asc": False,
"clearable": True,
"column": "region",
"key": "2s98dfu",
"metric": "sum__SP_POP_TOTL",
"multiple": False,
},
{
"asc": False,
"clearable": True,
"key": "li3j2lk",
"column": "country_name",
"metric": "sum__SP_POP_TOTL",
"multiple": True,
},
],
),
),
Slice(
slice_name="World's Population",
viz_type="big_number",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="2000",
viz_type="big_number",
compare_lag="10",
metric="sum__SP_POP_TOTL",
compare_suffix="over 10Y",
),
),
Slice(
slice_name="Most Populated Countries",
viz_type="table",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="table",
metrics=["sum__SP_POP_TOTL"],
groupby=["country_name"],
),
),
Slice(
slice_name="Growth Rate",
viz_type="line",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="line",
since="1960-01-01",
metrics=["sum__SP_POP_TOTL"],
num_period_compare="10",
groupby=["country_name"],
),
),
Slice(
slice_name="% Rural",
viz_type="world_map",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="world_map",
metric="sum__SP_RUR_TOTL_ZS",
num_period_compare="10",
secondary_metric=secondary_metric,
),
),
Slice(
slice_name="Life Expectancy VS Rural %",
viz_type="bubble",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="bubble",
since="2011-01-01",
until="2011-01-02",
series="region",
limit=0,
entity="country_name",
x="sum__SP_RUR_TOTL_ZS",
y="sum__SP_DYN_LE00_IN",
size="sum__SP_POP_TOTL",
max_bubble_size="50",
adhoc_filters=[
{
"clause": "WHERE",
"expressionType": "SIMPLE",
"filterOptionName": "2745eae5",
"comparator": [
"TCA",
"MNP",
"DMA",
"MHL",
"MCO",
"SXM",
"CYM",
"TUV",
"IMY",
"KNA",
"ASM",
"ADO",
"AMA",
"PLW",
],
"operator": "NOT IN",
"subject": "country_code",
}
],
),
),
Slice(
slice_name="Rural Breakdown",
viz_type="sunburst",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="sunburst",
groupby=["region", "country_name"],
since="2011-01-01",
until="2011-01-02",
metric=metric,
secondary_metric=secondary_metric,
),
),
Slice(
slice_name="World's Pop Growth",
viz_type="area",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="1960-01-01",
until="now",
viz_type="area",
groupby=["region"],
metrics=metrics,
),
),
Slice(
slice_name="Box plot",
viz_type="box_plot",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="1960-01-01",
until="now",
whisker_options="Min/max (no outliers)",
x_ticks_layout="staggered",
viz_type="box_plot",
groupby=["region"],
metrics=metrics,
),
),
Slice(
slice_name="Treemap",
viz_type="treemap",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="1960-01-01",
until="now",
viz_type="treemap",
metrics=["sum__SP_POP_TOTL"],
groupby=["region", "country_code"],
),
),
Slice(
slice_name="Parallel Coordinates",
viz_type="para",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="2011-01-01",
until="2012-01-01",
viz_type="para",
limit=100,
metrics=["sum__SP_POP_TOTL", "sum__SP_RUR_TOTL_ZS", "sum__SH_DYN_AIDS"],
secondary_metric="sum__SP_POP_TOTL",
series="country_name",
),
),
]
dashboard_positions = {
"CHART-36bfc934": {
"children": [],
"id": "CHART-36bfc934",
"meta": {"chartId": 40, "height": 25, "sliceName": "Region Filter", "width": 2},
"type": "CHART",
},
"CHART-37982887": {
"children": [],
"id": "CHART-37982887",
"meta": {
"chartId": 41,
"height": 25,
"sliceName": "World's Population",
"width": 2,
},
"type": "CHART",
},
"CHART-17e0f8d8": {
"children": [],
"id": "CHART-17e0f8d8",
"meta": {
"chartId": 42,
"height": 92,
"sliceName": "Most Populated Countries",
"width": 3,
},
"type": "CHART",
},
"CHART-2ee52f30": {
"children": [],
"id": "CHART-2ee52f30",
"meta": {"chartId": 43, "height": 38, "sliceName": "Growth Rate", "width": 6},
"type": "CHART",
},
"CHART-2d5b6871": {
"children": [],
"id": "CHART-2d5b6871",
"meta": {"chartId": 44, "height": 52, "sliceName": "% Rural", "width": 7},
"type": "CHART",
},
"CHART-0fd0d252": {
"children": [],
"id": "CHART-0fd0d252",
"meta": {
"chartId": 45,
"height": 50,
"sliceName": "Life Expectancy VS Rural %",
"width": 8,
},
"type": "CHART",
},
"CHART-97f4cb48": {
"children": [],
"id": "CHART-97f4cb48",
"meta": {
"chartId": 46,
"height": 38,
"sliceName": "Rural Breakdown",
"width": 3,
},
"type": "CHART",
},
"CHART-b5e05d6f": {
"children": [],
"id": "CHART-b5e05d6f",
"meta": {
"chartId": 47,
"height": 50,
"sliceName": "World's Pop Growth",
"width": 4,
},
"type": "CHART",
},
"CHART-e76e9f5f": {
"children": [],
"id": "CHART-e76e9f5f",
"meta": {"chartId": 48, "height": 50, "sliceName": "Box plot", "width": 4},
"type": "CHART",
},
"CHART-a4808bba": {
"children": [],
"id": "CHART-a4808bba",
"meta": {"chartId": 49, "height": 50, "sliceName": "Treemap", "width": 8},
"type": "CHART",
},
"COLUMN-071bbbad": {
"children": ["ROW-1e064e3c", "ROW-afdefba9"],
"id": "COLUMN-071bbbad",
"meta": {"background": "BACKGROUND_TRANSPARENT", "width": 9},
"type": "COLUMN",
},
"COLUMN-fe3914b8": {
"children": ["CHART-36bfc934", "CHART-37982887"],
"id": "COLUMN-fe3914b8",
"meta": {"background": "BACKGROUND_TRANSPARENT", "width": 2},
"type": "COLUMN",
},
"GRID_ID": {
"children": ["ROW-46632bc2", "ROW-3fa26c5d", "ROW-812b3f13"],
"id": "GRID_ID",
"type": "GRID",
},
"HEADER_ID": {
"id": "HEADER_ID",
"meta": {"text": "World's Bank Data"},
"type": "HEADER",
},
"ROOT_ID": {"children": ["GRID_ID"], "id": "ROOT_ID", "type": "ROOT"},
"ROW-1e064e3c": {
"children": ["COLUMN-fe3914b8", "CHART-2d5b6871"],
"id": "ROW-1e064e3c",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"ROW-3fa26c5d": {
"children": ["CHART-b5e05d6f", "CHART-0fd0d252"],
"id": "ROW-3fa26c5d",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"ROW-46632bc2": {
"children": ["COLUMN-071bbbad", "CHART-17e0f8d8"],
"id": "ROW-46632bc2",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"ROW-812b3f13": {
"children": ["CHART-a4808bba", "CHART-e76e9f5f"],
"id": "ROW-812b3f13",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"ROW-afdefba9": {
"children": ["CHART-2ee52f30", "CHART-97f4cb48"],
"id": "ROW-afdefba9",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"DASHBOARD_VERSION_KEY": "v2",
}
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